It is known to equip vehicles with radar systems and/or cameras systems in order to characterize the environment surrounding a vehicle. Such systems are able to provide detection of objects in the vicinity of the vehicle, in particular in a forward-looking direction. In such systems, processing of image data and/or radar reflection data allows objects in the vicinity of the vehicle to be detected and characterized. Such object identification or detection can be used to detect static objects such as guardrails, walls, trees, boundaries, posts or stationary vehicles for example, or moving objects such as other vehicles or pedestrians. In such Advanced Driver Assisted Systems (ADAS systems) both cameras mounted on the vehicle and/or antenna arrays may be used to detect/identify and characterize such objects
Although methods are known for camera pan and tilt angle estimation as well as for camera height calibration and auto-calibration; prior art methods have tended to ignore the roll angle of the camera which had to be assumed negligible.
Recently there has been a survey about an Optical Flow based method of camera angle estimation which includes roll (Westerhoff, J., Lessmann, S. et al.: Development and Comparison of Homography based Estimation Techniques for Camera to Road Surface Orientation, 2016 IEEE IV Symposium, Gotenburg). However, since Optical Flow calculations are expensive in terms of computational power, this may not be convenient for low-cost embedded systems so there is still need for some simple roll estimation method.
As long as camera auto-calibration is performed for passenger cars only, this might not pose a major problem. Passenger cars are not expected to carry large loads which may change drastically over a working day thus affecting the roll or tilt (sideways). But for trucks which may service a number of customer locations over the day, loading and unloading freight may affect the tilt and thus roll perspective. Change in uneven freight weight clearly changes forces on the truck's suspension system thus all camera angles (including roll) change.
Moreover, as OEM expectations of vision algorithm precision increase with every new camera generation, the assumption of negligible roll angle may not even hold for passenger cars any longer. A camera which relies on a constant roll angle in its auto-calibration is at risk of reduced performance or malfunction. For example, current Lane Detection algorithms detect not only the ego lane but also neighboring lanes. These are of course at larger distances from the vehicle so roll angle inaccuracies introduce errors which are no longer negligible.
The suspension system of a truck can affect camera roll by itself in a dynamical manner. Pneumatic springs can be regulated up to +/−20 cm in height to compensate for heavy freight load (which in turn affects the height, too), or to improve driving comfort on bad conditioned ground. The latter can happen even in mid-drive, not only at loading stops.
The problem of camera roll angle calibration for truck now becomes important since the EU regulation 351/2012 LDWS requires in future all commercial trucks in the EU to be equipped with a Lane Departure Warning system and an Emergency Automatic Brake System. For camera based systems of this kind this clearly needs for increased precision in calibration, including camera roll angle.